Clinical and Laboratory Biomarkers in Paroxysmal Atrial Fibrillation: A Single Center Cross-Sectional Study

Sun, Weiping; Li, Haiwei; Wang, Zefeng; Wu, Yongquan; Du, Jia

doi:https://doi.org/10.1155/2022/7012377

Contrast Media & Molecular Imaging

On this page

Abstract Introduction Methods Results Discussion Conclusion Data Availability Conflicts of Interest Acknowledgments References Copyright Related Articles

Special Issue

Developments in High Content Cellular Imaging Systems for Molecular Devices

View this Special Issue

Research Article | Open Access

Volume 2022 | Article ID 7012377 | https://doi.org/10.1155/2022/7012377

Clinical and Laboratory Biomarkers in Paroxysmal Atrial Fibrillation: A Single Center Cross-Sectional Study

Weiping Sun,^1,2Haiwei Li,²Zefeng Wang,²Yongquan Wu,²and Jia Du¹

Academic Editor: Mohammad Farukh Hashmi

Received12 May 2022

Revised03 Jun 2022

Accepted07 Jun 2022

Published29 Jun 2022

Abstract

The clinical risk profile of paroxysmal atrial fibrillation (pAF) patients is inconclusive. We aimed to identify clinical and laboratory biomarkers in patients with pAF and the differences in biomarkers among genders. A cross-sectional study was conducted with a total of 181 participants in a single center in Beijing Anzhen Hospital. The participants were grouped according to the presence of pAF and sex differences, and clinical and laboratory results were collected and compared. The 181 participants had a mean age of 52.9 ± 15.1 years (pAF group, 60.4 ± 9.9 years, SR group, 48.3 ± 15.9 years, P < 0.05). Patients with pAF had significantly higher rates of age, left atrial (LA) diameter, haemoglobin (Hb) levels, tissue inhibitor of metalloproteinase-1 (TIMP-1), soluble tumour suppressor-2 (sST2), B-type natriuretic peptide (BNP) and indirect bilirubin (Ibil), mean haemoglobin concentration (MCHC), and hypertension (HTN) and smoking (P < 0.05). Multivariable logistic regression analysis revealed that age (OR = 1.075, 95% CI: 1.035–1.118, P < 0.0001), smoking (OR = 4.538, 95% CI: 1.559–13.205, P = 0.006), and MCHC (OR = 1.062, 95% CI: 1.019–1.106, P = 0.004) were independent predictive factors for pAF. Multivariable logistic regression analysis found that age (OR = 1.107, 95% CI: 1.016–1.206, P = 0.02) and Ibil level (OR = 2.303, 95% CI: 1.158–4.582, P = 0.017) were independent predictive factors of the occurrence of pAF in females; BNP (OR = 1.015, 95% CI: 1.002–1.029, P = 0.029) was an independent predictive variable of pAF in males. Age, smoking, and MCHC were independent predictive factors of pAF. BNP was an independent predictive biomarker of pAF in males, while in females, age and Ibil were independent predictive factors.

1. Introduction

Atrial fibrillation (AF) is the most common sustained arrhythmia encountered in clinical practice [1]. AF is associated with substantial morbidity and mortality, therefore leading to a significant burden on patients, societal health, and economic development [2]. The prevalence of atrial fibrillation in adults is currently estimated to be between 2% and 4%, and a recent study suggests that an estimated 1 in 3 people were at risk of developing atrial fibrillation by the age of 55 [3, 4]. Paroxysmal atrial fibrillation (pAF) has a higher incidence and risk in adults. At present, the mechanism of atrial fibrillation is not fully understood; the AF lifetime risk depends on age and genetic and clinical factors [1, 5]. Early intervention and modifiable risk factor control could reduce the incidence of AF; therefore, identifying the risk factors for pAF is of great significance for the prevention of AF.

Recent studies have revealed sex-related differences in the clinical presentation, pathogenesis, outcomes, and management of AF patients; specifically, the risk of developing AF is 1.5–2.0 times higher in males than in females [6]. The cumulative AF incidence increases after the age of 50 years in males and 60 years in females; however, the lifetime risk is similar (>30%) between the sexes [3].

Regarding biomarkers for AF, guidelines for patient management have shown that serum N-terminal prohormone B-type natriuretic peptide (NT-proBNP) is a more powerful biomarker than other clinical variables, as well as echocardiographic assessment (left atrial and ventricular diameters) [2]. Recent studies have also found that soluble isoform of ST2 (sST2), tissue inhibitor matrix metalloproteinase 1 (TIMP-1), high-sensitivity C-reactive protein (hs-CRP), and BNP are involved in the mechanism of AF. Unfortunately, the precise mechanisms and signaling pathways involved in the atrial remodeling processes of AF have not been completely elucidated, and data on biomarkers as predictors of pAF are limited. Therefore, the aim of this study was to investigate potential clinical biomarkers that could be used to predict the occurrence of pAF, providing a theoretical basis for AF interventions and to identify biomarker and risk factor differences in females and males with pAF.

2. Methods

2.1. Study Design

The purposes of this cross-sectional study were to explore (i) potential clinical and laboratory biomarkers that could be used in predicting paroxysmal AF and (ii) differences in biomarkers and their values between females and males with pAF. The independent variables of this study came from clinical and laboratory examinations.

2.2. Participants

This study was approved by the ethics committee of Beijing Anzhen Hospital. In this cross-sectional study, 68 consecutive inpatients referred to our hospital for paroxysmal atrial fibrillation were included from June 2018 to July 2019 at the Beijing Anzhen Hospital. Simultaneously, we enrolled 113 inpatients with sinus rhythm (SR) as controls. The inclusion criteria were age greater than 18 years; clinical diagnosis with pAF; informed consent to participate in this study; and submission of a signed informed consent form. The exclusion criteria were AF or other heart diseases after inclusion in the SF group; diagnosis with persistent or permanent atrial fibrillation; presence of malignant tumors with a life expectancy shorter than 1 year; or other end-stage diseases. The definition of paroxysmal AF was AF that terminates spontaneously or with intervention within 7 days of onset; it was diagnosed based on 12-lead electrocardiogram and 24-hour ambulatory electrocardiogram.

2.3. Data Collection

General data of patients were collected, including age, sex, hypertension, coronary artery disease (CAD), diabetes, heart failure, and other indicators. At the same time, the clinical examination and laboratory results of the patients were collected, including white blood cell (WBC), red blood cell (RBC), platelet count (PLT), haemoglobin (Hb), fasting blood glucose, fibrinogen, D-dimer, creatinine, urea, uric acid (UA), aspartate transaminase (AST), alanine aminotransferase (ALT), lipoprotein, total cholesterol (TC), triacylglycerol (TG), and low-density lipoprotein (LDL). Left atrium (LA) diameter, left ventricular end-systolic dimension (LVESD), left ventricular end-diastolic dimension (LVEDD), and left ventricular ejection fraction (LVEF) were measured. For the blood markers, from the same blood sampling as TIMP-1, sST2, hs-CRP, and BNP, for other assessments, within 6 h of blood sampling. The CHA2DS2-VASc score and HAS-BLED score were also assessed in all participants.

2.4. Statistical Analysis

All statistical analyses were performed using Statistical Product and Service Solutions (SPSS) 20.0 (IBM Corp., Armonk, NY, USA). Continuous data are expressed as the mean ± standard deviation and were analyzed by Student’s t-test (comparisons of two groups). Nonnormally distributed continuous data are described as the median and interquartile range (IQR) and were compared using the Wilcoxon rank-sum test. Categorical variables are presented as numbers (percentages) and were analyzed by the Chi-square test or Fisher’s exact test, as appropriate. Parameters with P < 0.05 in the univariable analysis were included in the multivariable logistic regression analysis by the enter method. P < 0.05 was considered statistically significant.

3. Results

3.1. Baseline Clinical and Laboratory Participant Characteristics

A total of 181 participants were enrolled in our study. The 181 participants had a mean age of 52.9 ± 15.1 years (pAF group, 60.4 ± 9.9 years and SR group, 48.3 ± 15.9 years). Sixty-eight patients (46 males and 22 females) were included in the pAF group. The baseline clinical and laboratory characteristics of the participants are shown in Tables 1 and 2. Patients with pAF had significantly higher age, levels of Hb, creatinine, TIMP-1, sST2, Tbil, Ibil, and major histocompatibility complex (MHC), MCHC, and proportions of HTN and smoking (Figure 1). The levels of PLT, TP, Alb, and HDL-c in the pAF group were lower than those in the SR group.

3.2. Multivariable Logistic Regression Analysis of Factors Associated with pAF

Multivariable logistic regression analysis was performed in the pAF group following the univariable logistic regression analysis. The results revealed that age (OR = 1.075, 95% CI: 1.035–1.118, P < 0.0001), smoking (OR = 4.538, 95% CI: 1.559–13.205, P = 0.006), and MCHC (OR = 1.062, 95% CI: 1.019–1.106, P = 0.004) were independent predictive factors of pAF. Detailed results of the multivariable analysis are shown in Table 3.

3.3. Clinical Characteristics and Risk Factor Differences between Males and Females with pAF

As shown in Table 4, males had higher levels of Hb, CREA, TIMP-1, Tbil, Ibil, and MHC, MCHC, and proportion of smokers than females in the pAF group. Further multivariable logistic regression analysis (Table 5) showed that age (OR = 1.107, 95% CI: 1.016–1.206, P = 0.02) and Ibil (OR = 2.303, 95% CI: 1.158–4.582, P = 0.017) were independent predictive factors of the occurrence of pAF in females. BNP (OR = 1.015, 95% CI: 1.002–1.029, P = 0.029) was an independent predictive variable of pAF in males.

4. Discussion

The main findings of this study are as follows: (I) the pAF group was older and had higher levels of TIMP-1, sST2, and Ibil and a higher MCHC; moreover, smoking was more likely to be seen in the pAF group. (II) Multivariable logistic regression analysis showed that age, smoking, and MCHC were independent predictive factors of pAF. (III) Subgroup analysis based on sex showed that males with pAF had higher levels of Hb, CREA, TIMP-1, and Ibil, a higher MCHC, and a higher proportion of smokers than females; females with pAF had higher levels of PLT, UA, and HDL-c. (IV) Multivariable logistic regression analysis found that age and Ibil were independent predictive factors of the occurrence of pAF in females; BNP was an independent predictive variable of pAF in males.

Risk factors for AF progression include age, heart failure (HF), hypertension, chronic kidney disease (CKD), diabetes mellitus (DM), previous stroke, and left atrial size, but the added predictive value of biomarkers is presently not well defined [7]. Studies have revealed that older age is associated with permanent AF, but the correlation with pAF is unclear [2, 8]. In our study, we found that the pAF group was older, and multivariable analysis revealed that age was an independent predictive factor for pAF in females. Meanwhile, the pAF group also had a larger LA size, which was consistent with a previous study [7]. This reflects, to some extent, that AF was also a marker of atrial structural remodeling.

Serum ST2 is a member of the interleukin-1 receptor family, and circulating sST2 concentrations are believed to reflect cardiovascular stress and cardiac fibrosis. Receptor ST2L is involved in the inhibition of cardiac hypertrophy and fibrosis [9]. High concentrations of sST2 can prevent this protective effect and lead to cardiac fibrosis, which is linked to atrial fibrillation via arrhythmogenic structural remodeling [10]. However, the role of soluble ST2 in pAF has not been previously explored. Okar and colleagues reported that soluble ST2, as measured prior to an ablation procedure, is an independent parameter for predicting AF recurrence [11]. Other studies have suggested that sST2 was associated with LA volume enlargement, the LA low-voltage zone, and LV myocardium abnormalities in patients with paroxysmal AF [12, 13]. Similar to the above findings, our results indicate that the median sST2 concentration of the pAF group was 12.76 ± 4.73 ng/ml, higher than that of the SR group.

Atrial structural remodeling and maintenance of the extracellular space includes not only synthesis but also coordinated degradation of extracellular matrix (ECM) proteins (matrix metalloproteinases (MMPs) and TIMPs). Under normal circumstances, TIMP-1 inhibits the enzymatic activity of MMP-9 and regulates ECM turnover. However, the levels of TIMP-1 in pAF patients are not yet known. Paulina et al. found that the levels of TIMP-1 in pAF patients are elevated [14]. In contrast, another study showed that TIMP-1 serum levels were lower in paroxysmal and persistent AF [15]. In the current study, the pAF group also had elevated TIMP-1, but the reason for the difference in TIMP-1 levels was unclear. An imbalance between MMPs and TIMPs may reflect abnormal turnover of the ECM and therefore results in atrial remodeling and fibrosis.

NT-proBNP has been reported to be a predictor of AF in atrial fibrillation guidelines, but the use of baseline BNP levels as a predictor for pAF is inconclusive [2]. In the present study, the pAF group had a higher level of BNP than the SR group, and further multivariable analysis showed that BNP was an independent predictive variable of pAF in males. Several potential mechanisms can account for the association between increased AF burden and BNP levels. BNP is secreted predominantly from ventricular myocytes, and a small amount of BNP release from the atrium occurs in patients with AF as a response to atrial stretch. Elevated BNP levels in AF patients may also reflect increased left atrial pressure and stretch, being independent of the ventricular secretion of BNP. Therefore, increased mechanical atrial stretch itself promotes the secretion of BNP, and BNP might predict both the frequency and progression of AF [16, 17]. This reflects a mechanism by which increased left atrial pressure of AF in men is different from that in women.

Serum bilirubin, which is the catabolic product of the heme catabolic pathway, has long been used as a biomarker for hepatic metabolic and excretory capacity. Experimental evidence has shown that bilirubin might act as a powerful antioxidant and anti-inflammatory agent in biological functions and is associated with different cardiovascular diseases [18]. In patients with coronary artery disease, bilirubin levels have an inverse relationship with cardiovascular events [19]. In contrast, the bilirubin level has been positively correlated with heart failure and pulmonary hypertension in clinical observation studies [20, 21]. The bilirubin level in AF patients is unclear. One clinical study found that higher serum bilirubin levels were associated with AF recurrence in paroxysmal AF patients following catheter ablation [22]. We came to similar results, in which Tbil and Ibil were higher in the pAF group. In addition, we also observed that Ibil was an independent predictive factor of pAF in females. In patients with pAF, bilirubin, as an anti-inflammatory and antioxidant substance, may be compulsorily increased.

Anemia is known to be associated with poor prognosis in cardiovascular diseases. Relative hypochromia of erythrocytes, defined as a reduced MCHC, is a surrogate of iron deficiency [23]. Hammadah et al. [24] indicated that a lower MCHC is an independent predictor of increased mortality risk in nonanemic patients with HF. Kleber et al. [25] also reported that MCHC is a common and strong independent predictor of increased mortality in acute HF after adjusting for anemia. Nevertheless, there has been no research on the association between MCHC and AF until now. In the present study, patients with pAF had a larger MCHC than the SR group, and a higher MCHC was an independent predictive factor of pAF. The reason may be that patients with pAF have intermittent episodes of arrhythmia and transient abnormal heart function, which leads to a corresponding hypoxia-induced haemoglobin increase.

This study has limitations. First, this was a cross-sectional study, and there was no patient follow-up; therefore, these results may be prone to selection or information bias. Second, this was a single-center study, and the sample size was small; thus, it is not known whether these findings are generalizable, and thus, they need to be further confirmed in the future. Third, the biomarkers of inflammatory and oxidative stress were limited, and the characteristics were evaluated at only one time point.

5. Conclusion

In pAF patients, age, smoking, and MCHC are independent predictive factors of pAF. BNP is an independent predictive biomarker of pAF in males; age and Ibil are independent predictive factors of the occurrence of pAF in females.

Data Availability

The datasets used and analyzed during the current study are available from the corresponding author on reasonable request.

Conflicts of Interest

The authors declared no conflicts of interest.

Acknowledgments

This work was supported by the Beijing Hospitals Authority Clinical Medicine Development of Special Funding Support (Grant no. XMLX202112).

References

E. J. Benjamin, P. Muntner, A. Alonso et al., “Heart Disease and stroke Statistics-2019 Update: a report from the American heart association,” Circulation, vol. 139, no. 10, pp. e56–e528, 2019.
View at: Publisher Site | Google Scholar
G. Hindricks, T. Potpara, N. Dagres et al., “ESC Guidelines for the diagnosis and management of atrial fibrillation developed in collaboration with the European Association for Cardio-Thoracic Surgery (EACTS): the Task Force for the diagnosis and management of atrial fibrillation of the European Society of Cardiology (ESC) Developed with the special contribution of the European Heart Rhythm Association (EHRA) of the ESC,” European Heart Journal, vol. 42, no. 5, pp. 373–498, 2021.
View at: Google Scholar
C. Magnussen, T. J. Niiranen, F. M. Ojeda et al., “Sex Differences and Similarities in atrial fibrillation epidemiology, risk factors, and mortality in Community cohorts,” Circulation, vol. 136, no. 17, pp. 1588–1597, 2017.
View at: Publisher Site | Google Scholar
L. Staerk, B. Wang, S. R. Preis et al., “Lifetime risk of atrial fibrillation according to optimal, borderline, or elevated levels of risk factors: cohort study based on longitudinal data from the Framingham Heart Study,” BMJ, vol. 361, p. k1453, 2018.
View at: Publisher Site | Google Scholar
V. Allan, S. Honarbakhsh, J. P. Casas et al., “Are cardiovascular risk factors also associated with the incidence of atrial fibrillation? A systematic review and field synopsis of 23 factors in 32 population-based cohorts of 20 million participants,” Thrombosis & Haemostasis, vol. 117, no. 5, pp. 837–850, 2017.
View at: Publisher Site | Google Scholar
D. Ko, F. Rahman, R. B. Schnabel, X. Yin, E. J. Benjamin, and I. E. Christophersen, “Atrial fibrillation in women: epidemiology, pathophysiology, presentation, and prognosis,” Nature Reviews Cardiology, vol. 13, no. 6, pp. 321–332, 2016.
View at: Publisher Site | Google Scholar
H. Deng, Y. Bai, A. Shantsila, L. Fauchier, T. S. Potpara, and G. Y. H. Lip, “Clinical scores for outcomes of rhythm control or arrhythmia progression in patients with atrial fibrillation: a systematic review,” Clinical Research in Cardiology, vol. 106, no. 10, pp. 813–823, 2017.
View at: Publisher Site | Google Scholar
G. Boriani, M. Proietti, C. Laroche et al., “Contemporary stroke prevention strategies in 11 096 European patients with atrial fibrillation: a report from the EURObservational research Programme on atrial fibrillation (EORP-AF) long-term general Registry,” Europace: European pacing, arrhythmias, and cardiac electrophysiology: journal of the working groups on cardiac pacing, arrhythmias, and cardiac cellular electrophysiology of the European Society of Cardiology, vol. 20, no. 5, pp. 747–757, 2018.
View at: Publisher Site | Google Scholar
J. E. Ho, P. Sritara, C. R. DeFilippi, and T. J. Wang, “Soluble ST2 testing in the general population,” The American Journal of Cardiology, vol. 115, no. 7, pp. 22B–5B, 2015.
View at: Publisher Site | Google Scholar
S. Nortamo, O. Ukkola, T. Kenttä et al., “Association of sST2 and hs-CRP levels with new-onset atrial fibrillation in coronary artery disease,” International Journal of Cardiology, vol. 248, pp. 173–178, 2017.
View at: Publisher Site | Google Scholar
S. Okar, O. Kaypakli, D. Y. Koç, and M. Koc, “Fibrosis marker soluble ST2 predicts atrial fibrillation recurrence after Cryoballoon catheter ablation of Nonvalvular paroxysmal atrial fibrillation,” Korean Circulation Journal, vol. 48, no. 10, pp. 920–929, 2018.
View at: Publisher Site | Google Scholar
L. Zhao, S. Li, C. Zhang et al., “Cardiovascular magnetic resonance-determined left ventricular myocardium impairment is associated with C-reactive protein and ST2 in patients with paroxysmal atrial fibrillation,” Journal of Cardiovascular Magnetic Resonance, vol. 23, no. 1, p. 30, 2021.
View at: Publisher Site | Google Scholar
Z. Wang, L. Cheng, J. Zhang et al., “Serum-soluble ST2 is a Novel biomarker for evaluating left atrial low-voltage zone in paroxysmal atrial fibrillation,” Medical Science Monitor: International Medical Journal of Experimental and Clinical Research, vol. 26, Article ID e926221, 2020.
View at: Publisher Site | Google Scholar
P. Wakula, B. Neumann, J. Kienemund et al., “CHA2DS2-VASc score and blood biomarkers to identify patients with atrial high-rate episodes and paroxysmal atrial fibrillation,” Europace: European pacing, arrhythmias, and cardiac electrophysiology: journal of the working groups on cardiac pacing, arrhythmias, and cardiac cellular electrophysiology of the European Society of Cardiology, vol. 19, no. 4, pp. 544–551, 2017.
View at: Publisher Site | Google Scholar
A. E. Stanciu, R. G. Vatasescu, M. M. Stanciu, N. Serdarevic, and M. Dorobantu, “The role of pro-fibrotic biomarkers in paroxysmal and persistent atrial fibrillation,” Cytokine, vol. 103, pp. 63–68, 2018.
View at: Publisher Site | Google Scholar
M. Xu, F. Liu, Z. X. Ge, J. M. Li, X. Xie, and J. H. Yang, “Functional studies of left atrium and BNP in patients with paroxysmal atrial fibrillation and the prediction of recurrence after CPVA,” European Review for Medical and Pharmacological Sciences, vol. 24, no. 9, pp. 4997–5007, 2020.
View at: Publisher Site | Google Scholar
T. Inohara, S. Kim, K. Pieper et al., “B-type natriuretic peptide, disease progression and clinical outcomes in atrial fibrillation,” Heart, vol. 105, no. 5, pp. 370–377, 2019.
View at: Publisher Site | Google Scholar
A. Ayer, A. Zarjou, A. Agarwal, and R. Stocker, “Heme Oxygenases in cardiovascular health and Disease,” Physiological Reviews, vol. 96, no. 4, pp. 1449–1508, 2016.
View at: Publisher Site | Google Scholar
Y.-H. Chen, L.-Y. Chau, J.-W. Chen, and S.-J. Lin, “Serum bilirubin and ferritin levels link heme oxygenase-1 gene promoter polymorphism and susceptibility to coronary artery disease in diabetic patients,” Diabetes Care, vol. 31, no. 8, pp. 1615–1620, 2008.
View at: Publisher Site | Google Scholar
B. Szygula-Jurkiewicz, R. Wojnicz, A. Lekston et al., “Effect of elevated bilirubin levels on the long-term outcome in patients with chronic heart failure due to hypertension,” Pol Arch Med Wewn, vol. 117, no. 5-6, pp. 227–233, 2007.
View at: Google Scholar
X.-Q. Xu, Z.-C. Lv, Q.-Q. Liu et al., “Direct bilirubin: a new risk factor of adverse outcome in idiopathic pulmonary arterial hypertension,” International Journal of Cardiology, vol. 228, pp. 895–899, 2017.
View at: Publisher Site | Google Scholar
S.-C. Chen, F.-P. Chung, T.-F. Chao et al., “A link between bilirubin levels and atrial fibrillation recurrence after catheter ablation,” Journal of the Chinese Medical Association, vol. 82, no. 3, pp. 175–178, 2019.
View at: Publisher Site | Google Scholar
D. O. Okonko, A. K. J. Mandal, C. G. Missouris, and P. A. Poole-Wilson, “Disordered iron Homeostasis in chronic heart failure,” Journal of the American College of Cardiology, vol. 58, no. 12, pp. 1241–1251, 2011.
View at: Publisher Site | Google Scholar
M. Hammadah, M.-L. Brennan, Y. Wu, S. L. Hazen, and W. H. W. Tang, “Usefulness of relative hypochromia in risk Stratification for nonanemic patients with chronic heart failure,” The American Journal of Cardiology, vol. 117, no. 8, pp. 1299–1304, 2016.
View at: Publisher Site | Google Scholar
M. Kleber, N. Kozhuharov, Z. Sabti et al., “Relative hypochromia and mortality in acute heart failure,” International Journal of Cardiology, vol. 286, pp. 104–110, 2019.
View at: Publisher Site | Google Scholar

Copyright

Copyright © 2022 Weiping Sun et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

PDF Download Citation

Download other formats

Order printed copies

Views

191

Downloads

300

Citations